AI Summary
5 min readIn a recent episode of Odd Lots, hosts Joe Weisenthal and Tracy Alloway spoke with Justin Solomon, Associate Dean of Engineering Education at MIT, about how AI is upending the world of mathematics. Solomon, whose background includes applied math and a stint at Pixar, offered a grounded look at what AI can and cannot do in math, how the field is grappling with an identity crisis, and what this means for education. The conversation moved from the nature of mathematical proofs to the messy reality of who gets credit when an AI helps solve a problem.
What Mathematicians Actually Do
Solomon pushed back on the romanticized image of the lone genius scribbling on a chalkboard. "Math is a really social exercise," he said. Most mathematical work is collaborative, involving teams sitting in a room, talking through ideas, and working at a blackboard. The popular caricature from movies like Good Will Hunting misses this communal aspect.
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What you'll learn
- 1 (02:19) **Introducing the Crisis: AI and the Future of Mathematics** - Joe and Tracy frame the episode around the existential angst mathematicians feel as AI models get very good at solving problems, and set up their own basic questions about what advanced math actually is.
- 2 (08:42) **What Mathematicians Actually Do** - Justin Solomon explains the human motivation behind math, the difference between pure and applied work, and how math is a social, not solitary, exercise.
- 3 (14:41) **Applied Math in the Real World: Pixar and Fluids** - Justin uses his experience at Pixar to show how state-of-the-art math is used in movie effects, leading into a definition of the famous Navier-Stokes problem.
- 4 (19:27) **What is a Mathematical Proof?** - Justin breaks down the concept of a formal proof, explaining why proving something as simple as "2+2=4" requires multiple steps from agreed-upon axioms.
- 5 (20:56) **What AI Models Are (and Aren't) Doing** - Justin explains the current role of AI in mathematics, highlighting the critical distinction between generating a proof and verifying it.
- 6 (24:04) **Why Human Understanding Still Matters** - Justin argues that rigorous verification is critical for trust in engineering applications, using the example of simulating whether a building will collapse in an earthquake.
- 7 (26:11) **The Mathematician's Identity Crisis** - Justin describes the range of reactions in the math community, from despair to enthusiasm, and how the old metrics of success are breaking down.
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Guests on this episode
Show Notes
Last month, OpenAI announced that it had produced an AI-generated proof for the Navier-Stokes problem, one of the most famous unsolved questions in mathematics. LLMs used to be bad at counting, but now they are solving math problems that have stumped humans for decades. Meanwhile, at universities, the problem of AI in education continues: Now that LLMs can do a student's homework, teachers are struggling to keep up. It is clear that AI is very quickly changing how math is taught and how it is practiced by professionals. On this episode, Justin Solomon, who is the associate dean for engineering education at MIT, gives us a primer on how mathematicians (both pure and applied) are responding to all the advances in AI. He also explains what exactly the Navier-Stokes problem is and why the OpenAI proof is hard for even the pros to parse, what movies get wrong about how mathematicians do their jobs, and how he's changing his pedagogical approach in the age of AI.
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